Analysis of a multilevel diagnosis decision support system and its implications: a case study

Medical diagnosis can be performed in an automatic way with the use of computer-based systems or algorithms. Such systems are usually called diagnostic decision support systems (DDSSs) or medical diagnosis systems (MDSs). An evaluation of the performance of a DDSS called ML-DDSS has been performed in this paper. The methodology is based on clinical case resolution performed by physicians which is then used to evaluate the behavior of ML-DDSS. This methodology allows the calculation of values for several well-known metrics such as precision, recall, accuracy, specificity, and Matthews correlation coefficient (MCC). Analysis of the behavior of ML-DDSS reveals interesting results about the behavior of the system and of the physicians who took part in the evaluation process. Global results show how the ML-DDSS system would have significant utility if used in medical practice. The MCC metric reveals an improvement of about 30% in comparison with the experts, and with respect to sensitivity the system returns better results than the experts.

An approach for solving multi-level diagnosis in high sensitivity medical diagnosis systems through the application of semantic technologies

The capability of medical diagnosis systems to provide results in different situations depends on the modeling of the knowledge base. In the case of high sensitivity systems, the capability of having an adequate model allows to increase the accuracy of the system even in situations where the number of input elements is low. In this context the concept of multi-level diagnosis emerges, where a pathology can be assumed as a diagnostic element of another pathology (acting as a finding). In this paper this concept is studied in depth from the modeling point of view, providing a solution based on rule inference techniques modeled with semantic technologies, and allowing solving the problem generated by multi-level diagnosis.

SeDeLo: using semantics and description logics to support aided clinical diagnosis

Automated medical diagnosis systems based on knowledge-oriented descriptions have gained momentum with the emergence of semantic descriptions. The objective of this paper is to propose a normalized design that solves some of the problems which have been detected by authors in previous tools. The authors bring together two different technologies to develop a new clinical decision support system: description logics aimed at developing inference systems to improve decision support for the prevention, treatment and management of illness and semantic technologies. Because of its new design, the system is capable of obtaining improved diagnostics compared with previous efforts. However, this evaluation is more focused in the computational performance, giving as result that description logics is a good solution with small data sets. In this paper, we provide a well-structured ontology for automated diagnosis in the medical field and a three-fold formalization based on Description Logics with the use of Semantic Web technologies.